A fluid flow network consists of a group of flow branches, such as pipes and ducts that are joined together at a number of nodes. A fluid network can range from simple systems consisting of a few nodes and branches to very complex networks containing many flow branches, simulating valves, orifices, bends, pumps and turbines. In the analysis of existing or proposed fluid networks, some node pressures and temperatures are specified or known and are commonly referred to as initial conditions. An example of such a network, where simulation is an important tool, is in determining an accurate prediction of axial thrust in a liquid rocket engine turbopump. Such a network involves the flow of cryogenic fluid through extremely narrow passages, flow between rotating and stationary surfaces, phase changes, mixing of fluids and heat transfer. Propellant feed system designers are often required to analyze pressurization or blow down processes in flow circuits consisting of many series and parallel flow branches containing various pipe fittings and valves using cryogenic fluids. A simulation is used to determine all unknown nodal pressures, temperatures and branch flow rates by accounting for all parameters and conditions.
Available simulation systems are applicable to limited fluid conditions such as for steady-state, single phase incompressible flow. Because of the confidential proprietary nature of computer codes, it is not possible to extend their capability to satisfy other than the above mentioned conditions. Yet another limitation is that simulation code for fluid networks is traditionally for a specific purpose and for specific flow systems, such as to model the Space Shuttle Main Engine (SSME) turbopump. It is difficult to use simulation codes for new designs without making extensive changes in the original code, these changes can prove to be time consuming and inefficient. Therefore, the present Generalized Fluid System Simulation Program (GFSSP) has been developed as a general fluid flow system solver capable of handling phase changes, compressibility, mixture thermodynamics and transient operations, external body forces, such as gravity and centrifugal effects or centrifugal forces, in a complex flow network. The GFSSP simulation model may be constructed using a graphical user interface (GUI) in which various objects are represented by user selected icons or other appropriate graphical representations, and in which the interrelationship between the objects are represented by links.
The oldest simulation method for systematically solving a problem consisting of steady flow in a pipe network is the Hardy Cross method. Hardy Cross, “Analysis of Flow in Networks of Conduits or Conductors”, Univ. Ill., Bull. 286, November 1936. Not only is this method suited for solutions generated by hand, but it has also been widely employed for use in computer generated solutions. However, as computers allowed much larger networks to be analyzed, it became apparent that the convergence of the Hardy Cross method might be very slow or even fail to provide a solution in some cases. The main reason for this numerical difficulty is that the Hardy Cross method does not solve the system of equations simultaneously. It considers a portion of the flow network to determine the continuity and momentum errors. The head loss and the flow rates are corrected, and then it proceeds to an adjacent portion of the circuit. This process is continued until the whole circuit is completed. This sequence of operations is repeated until the continuity and momentum errors are minimized. It is evident that the Hardy Cross method belongs in the category of successive substitution methods, and it is therefore likely that it may encounter convergence difficulties for large circuits. In later years, the Newton-Raphson method has been utilized to solve large networks, and with improvements in algorithms based on the Newton-Raphson method, computer storage requirements are not much larger than those needed by the Hardy Cross method. See Jeppson, Ronald W., “Analysis of Flow in Pipe Networks”, Ann Arbor Science, 1977.
The flow of fluid in a rocket engine turbopump can be classified into two main categories. The flow through the impeller and turbine blade passages is designated as primary flow. Controlled leakage flow through bearings and seals for the purpose of axial thrust balance, bearing cooling and rotodynamic stability is referred to as secondary flow. Flows in the blade passages are modeled by solving Naiver-Stokes equations of mass, momentum and energy conservation in three dimensions. Naiver-Stokes methods, however, are not particularly suitable for modeling flow distribution in a complex network. Most of the available commercial software packages for solving flow networks are based either on the successive substitution method or on the Newton-Raphson method, and they are only applicable for a single phase incompressible fluid. Crane Company, “Flow of Fluids Through Valves, Fittings and Pipe”, Technical Paper No. 410, 1969; Kelix Software System, “Protopipe for Windows, Version 1.0, 1993-95. These are not suitable for modeling rocket engine turbopumps where mixing, phase change and rotational effects are present. Public domain computer programs have been developed in the aerospace industry to analyze the secondary flow in the SSME turbopumps. These programs use real gas properties to compute variable density in the flow passage. However, composite fluids, phase changes and rotational effects have proven difficult to model and simulate using existing software. See, e.g., Anderson, P. G., et al., “Fluid Flow Analysis of the SSME High Pressure Oxidizer Turbopump”, Lockheed Report No. LMSC-HREC TR D698083, August 1980.
For the reasons stated above, and for other reasons stated below which will become apparent to those skilled in the art upon reading and understanding the present specification, there is a need in the art for easier way to generate a simulation of a fluid network and for visualizing the fluid network and input/output parameters through graphical representations of the components and simulation result